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Data-driven approach has emerged as a popular research method for fault detection and diagnosis (FDD) of variable refrigerant flow system. However, one serious issue has been raised in recent studies, which shows that training classifiers with datasets which suffer of imbalanced class distributions can significantly influence the final classification accuracy. So, aiming at the imbalanced dataset environment problem of variable refrigerant flow (VRF) system based on data-driven fault detect and diagnosis, we conducted a study. First of all, through comparative research, it is concluded that the geometric mean accuracy of the fault detection in the VRF system has reduced an average of 42.49% due to the imbalanced data environment. Then we proposed a Principal Component Analysis-Synthetic Minority Oversampling Technique (PCA-SMOTE) method to save this problem, and verified the reliability and versatility of the proposed method with three types of modeling algorithms commonly used in VRF systems. The result shows that this method not only has obvious improvement effect, but also has strong versatility. In the end, we gradually reduce the number of minority from 50 to 10 to explore minimum fault data requirements of this method. The final result shows that this method can still ensure that common machine learning algorithms can effectively detect and diagnosis the VRF system fault when only 10 fault data are included. [ABSTRACT FROM AUTHOR]

• An improved deep learning model based on tree structure is proposed in VRF system. • Pearson's correlation coefficient combines with isolation forest for data preprocessing • The physical significance of outliers in VRF system is explained. • The IF-CF model does not require complex hyper-parameter optimization strategy. This paper presents a hybrid deep forest approach for outlier detection and fault diagnosis. Isolation forest algorithm is combined with Pearson's correlation coefficient for outlier detection. The physical significance of outliers detected by the proposed algorithm is explained by origin analysis, which is rarely mentioned in existing studies. In addition, a novel non-neural network deep learning model-cascade forest model is proposed to fault diagnosis of HVAC system for the first time to achieve high precision accuracy in low-dimensional features. The proposed approach is validated with the refrigerant charge fault of VRF system. The results show that the isolation forest algorithm can improve the performance of fault diagnosis model and the mainly outliers of VRF system are defrosting data. The IF-CF model has short operation time, and high accuracy in low-dimensional features. When the dimension drops to 6, the accuracy of the IF-CF model is 94.16%, which is 5.26%, 10.02%, 5.87% and 3.34% higher than the IF-MLP, IF-BPNN, IF-SVM and IF-LSTM models, respectively. Moreover, IF-CF model does not require complex hyper-parameter optimization strategy because its maximum accuracy difference in different hyper-parameters is 2.04%. This study is enlightening which may inspire the potential of outlier detection technology and deep learning in HVAC field. [ABSTRACT FROM AUTHOR]

• A novel fault diagnosis strategy is proposed using the GMM method. • The principal component analysis is selected for data dimensionality reduction. • Evaluations are made using four faults data of the VRF system experiments. • The established PCA-GMM has better fault diagnosis performance and reduced runtime. The timely fault diagnosis of HVAC systems is important for building energy saving, equipment maintenance and indoor comfort. The Gaussian mixture model method has rarely been studied in the fault diagnosis application of HVAC systems. Therefore, a novel fault diagnosis strategy is proposed based on the Gaussian mixture model (GMM) method for the variable refrigerant flow air-conditioning system. To reduce excessive input variables resulting in large model complexity and long running time, the principal component analysis approach (PCA) is used to perform data dimensionality reduction. Therefore, the fault diagnosis model combining the Gaussian mixture model and principal component analysis is established, which is evaluated using the four types of faults of the variable refrigerant flow system. These faults include refrigerant undercharge, refrigerant overcharge, outdoor unit fouling and four-way reversing valve faults. Experiments are carried out under three heating conditions. Results show that the PCA-GMM approach can effectively reduce the running time. Especially for the VVV type model, the running time is reduced from 176.78 s to 15.18 s. Meanwhile, established PCA-GMMs still have good fault diagnosis correct rates when the input data dimension is reduced. Especially, some PCA-GMMs have fault diagnosis correct rates of over 99% when the number of principal components exceeds 7. [ABSTRACT FROM AUTHOR]

• An online compressor liquid floodback fault diagnosis method is proposed for VRF system. • The proposed method fills the online liquid floodback fault diagnosis knowledge gap. • The online diagnosis results show the proposed method has extremely high accuracy. • The comparison results show that the BPNN method is better than CART method. Compressor liquid floodback not only can drop the performance of the variable refrigerant flow (VRF) system, but also may cause mechanical failure. Therefore, this is necessary to perform a timely online diagnosis of that fault. The present paper proposes an online compressor liquid floodback fault diagnosis method for VRF system based on back-propagation neural network (BPNN), which fills the online compressor liquid floodback fault diagnosis knowledge gap. The proposed method main article context as follow: Firstly, the sensors in VRF system record and save the data every three seconds to form the raw dataset. Secondly, after preprocessing the raw dataset, the correlation analysis is used to filter the data variables. Thirdly, the BPNN model is established by using back-propagation neural network algorithm. The testing dataset was used to verify the reliability of the model. The online dataset was used to test the model's online diagnostic capability, and its results were also compared and analyzed to the Classification and Regression Tree (CART) model. This result indicates that the BPNN method has a low degree of over-fitting and high reliability, and its online diagnostic accuracy is up to 99.48%. By comparing results of the method BPNN and CART, it shows that the former is superior to the latter, regardless of the diagnostic accuracy or the online diagnostic stability. [ABSTRACT FROM AUTHOR]

Highlights • A new charge fault diagnosis method for VRF system. • The temperature difference variable is introduced to improve the diagnosis effect. • Especially increases the distinction between normal and excess charge fault. Abstract VRF (Variable Refrigerant Flow) system is an efficient and energy-saving air-conditioning system. The VRF system performance is significantly affected by the quantity of refrigerant (charge). Both insufficient and excessive refrigerant charge will lead a dramatic decline of air conditioning system performance. Accordingly, it is necessary to forecast refrigerant charge in existing systems. This study proposed a charge fault diagnosis method based on expert modification C5.0 decision tree. Initially, C5.0 decision tree is used to diagnosis VRF system charge faults in nine different conditions. Furthermore, in order to reduce error rate of the decision tree, expert experience is used to modify the decision tree. And temperature differential variables are used to improve the diagnosis result. The result s of the C5.0 decision tree using temperature differential variables shows fault diagnosis error rates under expert experience guidance are 10% lower than without expert experience. Besides, this method an increased accuracy in classifies between excessive and normal charge states. [ABSTRACT FROM AUTHOR]